Image segmentation using a hierarchical student's‐
            <i>t</i>
            mixture model

Kong, Lingcheng; Hui, Zhang; Zheng, Yinqiang; Chen, Yunjie; Zhu, Jie; Wu, Q. M. Jonathan

doi:10.1049/iet-ipr.2017.0407

Cited by 10 publications

(10 citation statements)

References 29 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In formula (8), the maximum probability is estimated approximately after the conversion of the probability space, producing the coordinates of the ideal feature point.…”

Section: Extraction Of Salient Facial Features Figure 1 the Process mentioning

confidence: 99%

See 1 more Smart Citation

Detection of Head Raising Rate of Students in Classroom Based on Head Posture Recognition

Guo¹

2020

View full text Add to dashboard Cite

The proliferation of smart mobile terminals has weakened the attention and reduced the learning efficiency of students, making them more likely to lower their heads. To quantify the classroom participation, it is helpful to detect the head raising rate (HRR) of students in classroom. To this end, this paper puts forward a novel method to recognize the HRR of students in classroom. Based on the map of predicted facial features, an extraction method was developed for the salient facial features of students, and used to realize model matching between facial contour and facial organ. Next, the face orientation of each student was determined by soft label coding. After that, a multi-task convolutional neural network (CNN) was constructed to detect the HRR of students. The authors also explained the regularization of the loss function, and the steps of target detection. The proposed method was proved effective through experiments. The research results provide a reference for the application of head posture recognition in other fields.

show abstract

“…In formula (8), the maximum probability is estimated approximately after the conversion of the probability space, producing the coordinates of the ideal feature point.…”

Section: Extraction Of Salient Facial Features Figure 1 the Process mentioning

confidence: 99%

“…In the learning process, the line of sight, head posture, and facial expressions of a student can reflect his/her cognitive and emotional participations in the classroom. These non-verbal data can be obtained by corresponding algorithms [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Detection of Head Raising Rate of Students in Classroom Based on Head Posture Recognition

Guo¹

2020

View full text Add to dashboard Cite

show abstract

“…Step 3: M-step: Compute the mean μ j1 , covariance Σ j1 , scale parameter γ j2 , and location parameter χ j2 by using (30), (31), (29), and (28), respectively. Estimate the prior ρ i j by using (32).…”

Section: Image Clustering Using a Non-symmetric Gaussian-cauchy Mixtumentioning

confidence: 99%

Image clustering algorithm using superpixel segmentation and non‐symmetric Gaussian–Cauchy mixture model

Zhu

Wang

et al. 2020

IET image process

View full text Add to dashboard Cite

In this study, an unsupervised clustering algorithm is proposed to label superpixel density images. Firstly, the authors propose a novel superpixel segmentation algorithm driven by a modified fuzzy C-means objective function, Kullback-Leibler (KL) divergence, and an entropy term, which generate superpixels with good boundary adherence and intensity homogeneity. In this model, the logarithm of Gaussian distribution as a new distance metric is used to improve the accuracy of boundary pixel classification, the KL divergence is applied to regularise the fuzzy objective function. Based on this model, the generated superpixel intensity images with a highly distinctive background colour from the colour of the target are obtained. Grouping cues generated by superpixels can affect the performance of image clustering greatly. Next, according to the small amount of clustering data generated by the superpixel intensity images, they construct a non-symmetric mixture model based on a mixture of Gaussian distribution and Cauchy distribution for implementing image clustering. Thus, clustering of colour images is transformed into clustering of these newly generated data. The advantage of this model is its well adaption to different shapes of observed data. Experimental results on publicly available data sets are provided to demonstrate the effectiveness of the proposed algorithm. 12 IET Image Process.

show abstract

“…Image segmentation is one of the key steps of image processing for image analysis [1], the aim of segmentation is to select specific features out of an image from the background. Until recently, extensive studies have been made and many techniques have been proposed, among which the active contour models (ACMs) have become increasingly popular.…”

Section: Introductionmentioning

confidence: 99%

Local difference‐based active contour model for medical image segmentation and bias correction

Niu

Cao

2019

IET image process

View full text Add to dashboard Cite

This study proposes a local bias field and difference estimation (LBDE) model for medical image segmentation and bias field correction. Firstly, the LBDE model uses a linear combination of a given set of smooth orthogonal basis functions, which is called Chebyshev polynomial, to estimate the bias field. Then, a clustering criterion function is defined by considering the difference between the measured image and approximated image in a small region. By applying this difference in the local region, the LBDE model can obtain accurate segmentation results and estimation of the bias field. Finally, the energy functional is incorporated into a level set formulation with a regularisation term, and it is minimised via the level set evolution process. The LBDE model first appears as a two‐phase model and then extends to the multi‐phase one. Extensive experiments on medical images demonstrate that the LBDE model achieves more precise segmentation results in terms of Jaccard similarity and dice similarity coefficient than the comparative models. Therefore the proposed model can increase the segmentation accuracy and robustness to noise.

show abstract

Image segmentation using a hierarchical student's‐ t mixture model

Cited by 10 publications

References 29 publications

Detection of Head Raising Rate of Students in Classroom Based on Head Posture Recognition

Detection of Head Raising Rate of Students in Classroom Based on Head Posture Recognition

Image clustering algorithm using superpixel segmentation and non‐symmetric Gaussian–Cauchy mixture model

Local difference‐based active contour model for medical image segmentation and bias correction

Contact Info

Product

Resources

About